From 0 to 1: Machine Learning, NLP & Python

Machine learning techniques that you can put to work today.

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Course curriculum

1

Introduction

2

Jump right in : Machine learning for Spam detection

3

Solving Classification Problems

4

Clustering as a Form of Unsupervised Learning

5

Association Detection

6

Dimensionality Reduction

7

Regression as a Form of Supervised Learning

8

Natural Language Processing and Python

9

Sentiment Analysis

10

Decision Trees

11

A Few Useful Things to Know About Overfitting

12

Random Forests

13

Recommendation Systems

14

Recommendation Systems in Python

15

A Taste of Deep Learning and Computer Vision

16

Quizzes

You, this course and us
A sneak peek at what's coming up

Solving problems with computers
Machine Learning: Why should you jump on the bandwagon?
Plunging In - Machine Learning Approaches to Spam Detection
Spam Detection with Machine Learning Continued
Get the Lay of the Land : Types of Machine Learning Problems
Downloads

This course is taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

The course is very visual : most of the techniques are explained with the help of animations to help you understand better.

This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python.

The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.

A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.